Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Elsässerstr. 2, 79110 Freiburg, Germany.
Institute for Evidence in Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Breisacher Str. 153, 79110 Freiburg, Germany.
J Clin Epidemiol. 2019 Jan;105:68-79. doi: 10.1016/j.jclinepi.2018.09.010. Epub 2018 Sep 22.
In epidemiologic cohort studies with missing disease information due to death (MDID), conventional analyses right-censoring death cases at the last observation or at death may yield significant bias in relative risk and hazard ratio estimates. The aim of this study was to investigate susceptibility to this bias and assess its potential direction and magnitude.
Literature review of selected epidemiologic, geriatric, and environmental journals in 2011-2012 and simulation study of various conventional approaches to handling missing disease data. A study was considered susceptible to MDID bias if disease information was collected at follow-up visits only, and a conventional analysis was performed on the data.
Of 125 identified studies, 58 (46.4%, 95% confidence interval [CI]: 37.7-55.1%) were classified as susceptible to MDID bias, of which six (10.3%, 95% CI: 2.5-18.2%) attempted to address this in sensitivity analyses. The simulation revealed that depending on the analytic strategy for handling missing disease data, the potential exists for significant under- or over-estimation of risk factor effect estimates.
Awareness of MDID bias is important as more adequate analysis methods exist permitting an unbiased analysis. Recommendations for better reporting and analysis of MDID are provided.
在由于死亡而导致疾病信息缺失(MDID)的流行病学队列研究中,传统的分析方法将死亡病例在最后一次观察或死亡时右删失,可能会导致相对风险和危险比估计值产生显著偏倚。本研究旨在探讨易感性以及评估其潜在的方向和程度。
2011-2012 年对选定的流行病学、老年医学和环境期刊进行文献回顾,以及对各种处理缺失疾病数据的常规方法进行模拟研究。如果疾病信息仅在随访期间收集,并且对数据进行常规分析,则认为该研究易受 MDID 偏倚的影响。
在 125 项确定的研究中,58 项(46.4%,95%置信区间 [CI]:37.7-55.1%)被归类为易受 MDID 偏倚影响,其中 6 项(10.3%,95% CI:2.5-18.2%)试图在敏感性分析中解决这一问题。模拟结果表明,根据处理缺失疾病数据的分析策略,存在对风险因素效应估计值低估或高估的可能性。
认识到 MDID 偏倚很重要,因为存在更充分的分析方法可以进行无偏分析。提供了有关更好地报告和分析 MDID 的建议。